Investigation on Multi-Objective Optimization of Thermal-Hydraulic Performance for Enhanced Heat Transfer in Jet Impingement Cooling

Implementing efficient, clean, and energy-saving cooling solutions is important to decarbonize data centers. This study investigates a variable-spacing multijet direct-chip cooling device that increases the heat-transfer coefficient and improves temperature uniformity. The effects of the coolant flow rate, inlet temperature, pin-fin design parameters, and jet hole spacings on the thermal resistance, pressure drop, standard deviation of temperature, and Nusselt number are investigated. The dataset for the surrogate model construction is obtained based on computational fluid dynamics and Latin hypercube-sampling experimental designs. An artificial neural network model is developed with structural and thermal parameters as inputs and thermal resistance, pressure drop, and temperature uniformity as outputs. An algorithm called constrained multi-objective optimization based on the even search is used to find the solution. The optimization results show that the optimal design outperforms the initial design and has better performance specifications than those of existing studies for both thermal and hydraulic performances. Thus, it has good prospects for engineering applications. Herein, the optimization of variable-spacing multijet direct-chip cooling is investigated, enabling the chip to operate at a higher performance level.